Quantitative Imaging and Analysis Lab

An exciting publication on our collaborative research focusing on the innovative applications of 3D Bioprinting and Photon-Counting CT.

Gil CJ, Evans CJ, Li L, Allphin AJ, Tomov ML, Jin L, Vargas M, Hwang B, Wang J, Putaturo V, Kabboul G, Alam AS, Nandwani RK, Wu Y, Sushmit A, Fulton T, Shen M, Kaiser JM, Ning L, Veneziano R, Willet N, Wang G, Drissi H, Weeks ER, Bauser-Heaton HD, Badea CT, Roeder RK, Serpooshan V. Leveraging 3d Bioprinting And Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs. Adv Healthc Mater. 2023 Sep 14:e2302271. doi: 10.1002/adhm.202302271. PMID: 37709282.

Read the full paper here

A big thank you to our team and collaborators..

Nadkarni, R.; Clark, D.P.; Allphin, A.J.; Badea, C.T. A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images. Tomography 2023, 9, 1286-1302. https://doi.org/10.3390/tomography9040102

Our QIAL developed Multi-Channel CT Reconstruction (MCR) Toolkit addresses unique challenges associated with in vivo preclinical micro-CT and supporting the translation of these advancements to the clinical domain.

We have now published a paper in Medical Physics detailing our toolkit’s capabilities and functions:

Clark DP, Badea CT. MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Med. Phys.. 2023;1-22. https://doi.org/10.1002/mp.16532 

Our group contributed to these new papers from the co-clinical imaging research program (CIRP) :

1.Moore, S.M.; Quirk, J.D.; Lassiter, A.W.; Laforest, R.; Ayers, G.D.; Badea, C.T.; Fedorov, A.Y.; Kinahan, P.E.; Holbrook, M.; Larson, P.E.Z.; Sriram, R.; Chenevert, T.L.; Malyarenko, D.; Kurhanewicz, J.; Houghton, A.M.; Ross, B.D.; Pickup, S.; Gee, J.C.; Zhou, R.; Gammon, S.T.; Manning, H.C.; Roudi, R.; Daldrup-Link, H.E.; Lewis, M.T.; Rubin, D.L.; Yankeelov, T.E.; Shoghi, K.I. Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging. Tomography 2023, 9, 995-1009. https://doi.org/10.3390/tomography9030081

2.Gammon, S.T.; Cohen, A.S.; Lehnert, A.L.; Sullivan, D.C.; Malyarenko, D.; Manning, H.C.; Hormuth, D.A.; Daldrup-Link, H.E.; An, H.; Quirk, J.D.; Shoghi, K.; Pagel, M.D.; Kinahan, P.E.; Miyaoka, R.S.; Houghton, A.M.; Lewis, M.T.; Larson, P.; Sriram, R.; Blocker, S.J.; Pickup, S.; Badea, A.; Badea, C.T.; Yankeelov, T.E.; Chenevert, T.L. An Online Repository for Pre-Clinical Imaging Protocols (PIPs). Tomography 2023, 9, 750-758. https://doi.org/10.3390/tomography9020060

1)  A Allphin, R Nadkarni, D Clark, C T Badea. Ex vivo high-resolution hybrid micro-CT imaging using photon counting and energy integrating detectors. Proc. of SPIE Vol 12468, 124680V-1, 2023

2) DP Clark, FR Schwartz, A Euler, V Mergen, H Alkadhi, D Marin, CT Badea  Unsupervised learning of robust models for cardiac and photon-counting x-ray CT denoising, SPIE Medical Imaging 2023: Physics of Medical Imaging 12463, 329-337

3) AJ Allphin, DP Clark, T Thuering, P Bhandari, KB Ghaghada, CT Badea Spectral micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors, SPIE Medical Imaging 2023: Physics of Medical Imaging 12463, 153-159

In this study, we compared the performance of PC micro-CT imaging when using two types of PCDs. We have also demonstrated the ability of PCDs to separate a wide range of K-edge materials.

Alex Jeffrey Allphin, Darin P Clark, Thomas Thüring, Prajwal Bhandari, Ketan B Ghaghada, Cristian T Badea. Micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors. Physics in Medicine & Biology, 2023, DOI 10.1088/1361-6560/acc77e

QIAL members and the Bass Connections team, March 2023

Artist and T-shirts Designer: Nariman (Ali) Mahzarnia

This overview paper describes the ten co-clinical trials of investigators from eleven institutions who are supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group.

Examples of MRI and micro-CT images in the p53/MCA model and the schematics of the preclinical arm of the clinical trial organized at Duke.

Peehl, D.M.; Badea, C.T.; Chenevert, T.L.; Daldrup-Link, H.E.; Ding, L.; Dobrolecki, L.E.; Houghton, A.M.; Kinahan, P.E.; Kurhanewicz, J.; Lewis, M.T.; Li, S.; Luker, G.D.; Ma, C.X.; Manning, H.C.; Mowery, Y.M.; O’Dwyer, P.J.; Pautler, R.G.; Rosen, M.A.; Roudi, R.; Ross, B.D.; Shoghi, K.I.; Sriram, R.; Talpaz, M.; Wahl, R.L.; Zhou, R. Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials. Tomography 2023, 9, 657-680. https://doi.org/10.3390/tomography9020053